[MOT] add tracking pipeline (#4390)

* add tracking pipeline

* move pptracking to deploy

* update according to review

* refine class define

* add yaml cmake

deploy/pptracking/CMakeLists.txt

+cmake_minimum_required(VERSION 3.0)
+project(PaddleObjectDetector CXX C)
+
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support,defaultuseMKL."          ON)
+option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    ON)
+option(WITH_TENSORRT   "Compile demo with TensorRT."                                    OFF)
+
+SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
+SET(PADDLE_LIB_NAME "" CACHE STRING "libpaddle_inference")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
+SET(TENSORRT_INC_DIR "" CACHE PATH "Compile demo with TensorRT")
+SET(TENSORRT_LIB_DIR "" CACHE PATH "Compile demo with TensorRT")
+
+include(cmake/yaml-cpp.cmake)
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
+
+set(SRCS src/main.cc src/preprocess_op.cc src/pipeline.cc src/predictor.cc src/jde_predictor.cc src/sde_predictor.cc src/tracker.cc src/trajectory.cc src/lapjv.cpp src/base_predictor.cc src/postprocess.cc)
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+if (WITH_MKL)
+    ADD_DEFINITIONS(-DUSE_MKL)
+endif()
+
+if (NOT DEFINED PADDLE_DIR OR ${PADDLE_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set PADDLE_DIR with -DPADDLE_DIR=/path/paddle_influence_dir")
+endif()
+message("PADDLE_DIR IS:" ${PADDLE_DIR})
+
+if (NOT DEFINED OPENCV_DIR OR ${OPENCV_DIR} STREQUAL "")
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
+endif()
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+include_directories("${PADDLE_DIR}/")
+include_directories("${PADDLE_DIR}/third_party/install/protobuf/include")
+include_directories("${PADDLE_DIR}/third_party/install/glog/include")
+include_directories("${PADDLE_DIR}/third_party/install/gflags/include")
+include_directories("${PADDLE_DIR}/third_party/install/xxhash/include")
+if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/include")
+    include_directories("${PADDLE_DIR}/third_party/install/snappy/include")
+endif()
+if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/include")
+    include_directories("${PADDLE_DIR}/third_party/install/snappystream/include")
+endif()
+include_directories("${PADDLE_DIR}/third_party/boost")
+include_directories("${PADDLE_DIR}/third_party/eigen3")
+
+if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+    link_directories("${PADDLE_DIR}/third_party/install/snappy/lib")
+endif()
+if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+    link_directories("${PADDLE_DIR}/third_party/install/snappystream/lib")
+endif()
+
+link_directories("${PADDLE_DIR}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_DIR}/third_party/install/glog/lib")
+link_directories("${PADDLE_DIR}/third_party/install/gflags/lib")
+link_directories("${PADDLE_DIR}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_DIR}/paddle/lib/")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}")
+
+
+
+if (WIN32)
+  include_directories("${PADDLE_DIR}/paddle/fluid/inference")
+  include_directories("${PADDLE_DIR}/paddle/include")
+  link_directories("${PADDLE_DIR}/paddle/fluid/inference")
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${PADDLE_DIR}/paddle/include")
+  link_directories("${PADDLE_DIR}/paddle/lib")
+endif ()
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o2 -fopenmp -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+endif()
+
+# TODO let users define cuda lib path
+if (WITH_GPU)
+    if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
+
+if (NOT WIN32)
+  if (WITH_TENSORRT AND WITH_GPU)
+	  include_directories("${TENSORRT_INC_DIR}/")
+	  link_directories("${TENSORRT_LIB_DIR}/")
+  endif()
+endif(NOT WIN32)
+
+if (NOT WIN32)
+    set(NGRAPH_PATH "${PADDLE_DIR}/third_party/install/ngraph")
+    if(EXISTS ${NGRAPH_PATH})
+        include(GNUInstallDirs)
+        include_directories("${NGRAPH_PATH}/include")
+        link_directories("${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}")
+        set(NGRAPH_LIB ${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}/libngraph${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+endif()
+
+if(WITH_MKL)
+  include_directories("${PADDLE_DIR}/third_party/install/mklml/include")
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.lib
+            ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.lib)
+  else ()
+    set(MATH_LIB ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+            ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    execute_process(COMMAND cp -r ${PADDLE_DIR}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
+  endif ()
+  set(MKLDNN_PATH "${PADDLE_DIR}/third_party/install/mkldnn")
+  if(EXISTS ${MKLDNN_PATH})
+    include_directories("${MKLDNN_PATH}/include")
+    if (WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else ()
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    endif ()
+  endif()
+else()
+  set(MATH_LIB ${PADDLE_DIR}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+endif()
+
+
+if (WIN32)
+    if(EXISTS "${PADDLE_DIR}/paddle/fluid/inference/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}")
+        set(DEPS
+            ${PADDLE_DIR}/paddle/fluid/inference/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+    else()
+        set(DEPS
+            ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+    endif()
+endif()
+
+
+if (WIN32)
+    set(DEPS ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX})
+else()
+    set(DEPS ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX})
+endif()
+
+message("PADDLE_LIB_NAME:" ${PADDLE_LIB_NAME})
+message("DEPS:" $DEPS)
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags protobuf z xxhash yaml-cpp
+        )
+    if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+    if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+else()
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags_static libprotobuf xxhash libyaml-cppmt)
+    set(DEPS ${DEPS} libcmt shlwapi)
+    if (EXISTS "${PADDLE_DIR}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+    if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+endif(NOT WIN32)
+
+if(WITH_GPU)
+  if(NOT WIN32)
+    if (WITH_TENSORRT)
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+	    set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+add_executable(main ${SRCS})
+ADD_DEPENDENCIES(main ext-yaml-cpp)
+message("DEPS:" $DEPS)
+target_link_libraries(main ${DEPS})
+
+if (WIN32 AND WITH_MKL)
+    add_custom_command(TARGET main POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}.dll ./release/${PADDLE_LIB_NAME}.dll
+    )
+endif()
+
+if (WIN32)
+    add_custom_command(TARGET main POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_DIR}/paddle/lib/${PADDLE_LIB_NAME}.dll ./release/${PADDLE_LIB_NAME}.dll
+    )
+endif()

deploy/pptracking/cmake/yaml-cpp.cmake

+
+find_package(Git REQUIRED)
+
+include(ExternalProject)
+
+message("${CMAKE_BUILD_TYPE}")
+
+ExternalProject_Add(
+        ext-yaml-cpp
+        URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
+        URL_MD5 9542d6de397d1fbd649ed468cb5850e6
+        CMAKE_ARGS
+        -DYAML_CPP_BUILD_TESTS=OFF
+		-DYAML_CPP_BUILD_TOOLS=OFF
+        -DYAML_CPP_INSTALL=OFF
+        -DYAML_CPP_BUILD_CONTRIB=OFF
+		-DMSVC_SHARED_RT=OFF
+		-DBUILD_SHARED_LIBS=OFF
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+        -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+        -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
+        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+        -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        -DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
+        # Disable install step
+        INSTALL_COMMAND ""
+	    LOG_DOWNLOAD ON
+        LOG_BUILD 1
+)

deploy/pptracking/include/config_parser.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+#include <string>
+#include <map>
+
+#include "yaml-cpp/yaml.h"
+
+#ifdef _WIN32
+#define OS_PATH_SEP "\\"
+#else
+#define OS_PATH_SEP "/"
+#endif
+
+namespace PaddleDetection {
+
+// Inference model configuration parser
+class ConfigPaser {
+ public:
+  ConfigPaser() {}
+
+  ~ConfigPaser() {}
+
+  bool load_config(const std::string& model_dir,
+                   const std::string& cfg = "infer_cfg.yml") {
+    // Load as a YAML::Node
+    YAML::Node config;
+    config = YAML::LoadFile(model_dir + OS_PATH_SEP + cfg);
+
+    // Get runtime mode : fluid, trt_fp16, trt_fp32
+    if (config["mode"].IsDefined()) {
+      mode_ = config["mode"].as<std::string>();
+    } else {
+      std::cerr << "Please set mode, "
+                << "support value : fluid/trt_fp16/trt_fp32."
+                << std::endl;
+      return false;
+    }
+
+    // Get model arch: FairMot or YOLO/Picodet/LCNet for DeepSort
+    if (config["arch"].IsDefined()) {
+      arch_ = config["arch"].as<std::string>();
+    } else {
+      std::cerr << "Please set model arch,"
+                << "support value : FairMot, YOLO, PicoDet, LCNet etc"
+                << std::endl;
+      return false;
+    }
+
+    // Get min_subgraph_size for tensorrt
+    if (config["min_subgraph_size"].IsDefined()) {
+      min_subgraph_size_ = config["min_subgraph_size"].as<int>();
+    } else {
+      std::cerr << "Please set min_subgraph_size." << std::endl;
+      return false;
+    }
+    // Get draw_threshold for visualization
+    if (config["draw_threshold"].IsDefined()) {
+      draw_threshold_ = config["draw_threshold"].as<float>();
+    } else {
+      std::cerr << "Please set draw_threshold." << std::endl;
+      return false;
+    }
+    // Get Preprocess for preprocessing
+    if (config["Preprocess"].IsDefined()) {
+      preprocess_info_ = config["Preprocess"];
+    } else {
+      std::cerr << "Please set Preprocess." << std::endl;
+      return false;
+    }
+    // Get label_list for visualization
+    if (config["label_list"].IsDefined()) {
+      label_list_ = config["label_list"].as<std::vector<std::string>>();
+    } else {
+      std::cerr << "Please set label_list." << std::endl;
+      return false;
+    }
+
+    // Get use_dynamic_shape for TensorRT
+    if (config["use_dynamic_shape"].IsDefined()) {
+      use_dynamic_shape_ = config["use_dynamic_shape"].as<bool>();
+    } else {
+      std::cerr << "Please set use_dynamic_shape." << std::endl;
+      return false;
+    }
+
+    // Get conf_thresh for tracker
+    if (config["tracker"].IsDefined()) {
+      if (config["tracker"]["conf_thres"].IsDefined()) {
+        conf_thresh_ = config["tracker"]["conf_thres"].as<float>();
+      } else {
+        std::cerr << "Please set conf_thres in tracker." << std::endl;
+        return false;
+      }
+    }
+
+    // Get NMS for postprocess
+    if (config["NMS"].IsDefined()) {
+      nms_info_ = config["NMS"];
+    }
+    // Get fpn_stride in PicoDet
+    if (config["fpn_stride"].IsDefined()) {
+      fpn_stride_.clear();
+      for (auto item : config["fpn_stride"]) {
+        fpn_stride_.emplace_back(item.as<int>());
+      }
+    }
+
+    return true;
+  }
+  std::string mode_;
+  float draw_threshold_;
+  std::string arch_;
+  int min_subgraph_size_;
+  YAML::Node preprocess_info_;
+  YAML::Node nms_info_;
+  std::vector<std::string> label_list_;
+  std::vector<int> fpn_stride_;
+  bool use_dynamic_shape_;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection
+

deploy/pptracking/include/jde_predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include "paddle_inference_api.h" // NOLINT
+
+#include "include/preprocess_op.h"
+#include "include/config_parser.h"
+#include "include/utils.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+class JDEPredictor {
+ public:
+  explicit JDEPredictor(const std::string& device="CPU",
+                         const std::string& model_dir="",
+                         const double threshold=-1.,
+                         const std::string& run_mode="fluid",
+                         const int gpu_id=0,
+                         const bool use_mkldnn=false,
+                         const int cpu_threads=1,
+                         bool trt_calib_mode=false,
+                         const int min_box_area=200) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->min_box_area_ = min_box_area;
+
+    config_.load_config(model_dir);
+    this->min_subgraph_size_ = config_.min_subgraph_size_;
+    preprocessor_.Init(config_.preprocess_info_);
+    LoadModel(model_dir, run_mode);
+    this->conf_thresh_ = config_.conf_thresh_;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(
+    const std::string& model_dir,
+    const std::string& run_mode = "fluid");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+      const double threshold = 0.5,
+      MOTResult* result = nullptr,
+      std::vector<double>* times = nullptr);
+
+ private:
+  std::string device_ = "CPU";
+  float threhold = 0.5;
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_math_library_num_threads_ = 1;
+  int min_subgraph_size_ = 3;
+  bool trt_calib_mode_ = false;
+
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat& image_mat);
+  // Postprocess result
+  void Postprocess(
+      const cv::Mat dets, const cv::Mat emb,
+      MOTResult* result);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> bbox_data_;
+  std::vector<float> emb_data_;
+  double threshold_;
+  ConfigPaser config_;
+  float min_box_area_;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/include/lapjv.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+
+#ifndef LAPJV_H
+#define LAPJV_H
+
+#define LARGE 1000000
+
+#if !defined TRUE
+#define TRUE 1
+#endif
+#if !defined FALSE
+#define FALSE 0
+#endif
+
+#define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) {return -1;}
+#define FREE(x) if (x != 0) { free(x); x = 0; }
+#define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; }
+#include <opencv2/opencv.hpp>
+
+namespace PaddleDetection {
+
+typedef signed int int_t;
+typedef unsigned int uint_t;
+typedef double cost_t;
+typedef char boolean;
+typedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;
+
+int lapjv_internal(
+    const cv::Mat &cost, const bool extend_cost, const float cost_limit,
+    int *x, int *y);
+
+} // namespace PaddleDetection
+
+#endif // LAPJV_H
+

deploy/pptracking/include/pipeline.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <glog/logging.h>
+
+#include <iostream>
+#include <string>
+#include <vector>
+#include <numeric>
+#include <sys/types.h>
+#include <sys/stat.h>
+#include <math.h>
+#include <algorithm>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include "include/predictor.h"
+
+namespace PaddleDetection {
+
+class Pipeline {
+ public:
+  explicit Pipeline(const std::string& device,
+                  const double threshold,
+                  const std::string& output_dir,
+                  const std::string& run_mode="fluid",
+                  const int gpu_id=0,
+                  const bool use_mkldnn=false,
+                  const int cpu_threads=1,
+                  const bool trt_calib_mode=false,
+                  const bool count=false,
+                  const bool save_result=false) {
+    std::vector<std::string> input;
+    this->input_ = input;
+    this->device_ = device;
+    this->threshold_ = threshold;
+    this->output_dir_ = output_dir;
+    this->run_mode_ = run_mode;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->count_ = count;
+    this->save_result_ = save_result;
+  }
+
+
+  // Select model according to scenes, it must execute before Run()
+  void SelectModel(const std::string& scene="pedestrian",
+                   const bool tiny_obj=false,
+                   const bool is_mct=false);
+
+  // Set input, it must execute before Run()
+  void SetInput(std::string& input_video);
+
+  // Run pipeline
+  void Run();
+
+  void PredictSCT(const std::string& video_path);
+  void PredictMCT(const std::vector<std::string> video_inputs);
+
+  void PrintBenchmarkLog(std::vector<double> det_time, int img_num);
+
+ private:
+  std::vector<std::string> input_;
+  std::string device_;
+  double threshold_;
+  std::string output_dir_;
+  std::string track_model_dir_;
+  std::string det_model_dir_;
+  std::string reid_model_dir_;
+  std::string mct_model_dir_;
+  std::string run_mode_ = "fluid";
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_threads_ = 1;
+  bool trt_calib_mode_ = false;
+  bool count_ = false;
+  bool save_result_ = false;
+};
+
+} // namespace PaddleDetection

deploy/pptracking/include/postprocess.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include "include/utils.h"
+
+namespace PaddleDetection {
+
+// Generate visualization color
+cv::Scalar GetColor(int idx);
+
+// Visualize Tracking Results
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                     const MOTResult& results,
+                     const float fps, const int frame_id);
+
+// Pedestrian/Vehicle Counting
+void FlowStatistic(const MOTResult& results, const int frame_id,
+                   std::vector<int>* count_list, 
+                   std::vector<int>* in_count_list, 
+                   std::vector<int>* out_count_list);
+
+// Save Tracking Results
+void SaveResult(const MOTResult& results, const std::string& output_dir);
+
+} // namespace PaddleDetection

deploy/pptracking/include/predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include "paddle_inference_api.h" // NOLINT
+
+#include "include/preprocess_op.h"
+#include "include/config_parser.h"
+#include "include/jde_predictor.h"
+#include "include/sde_predictor.h"
+
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+class Predictor {
+ public:
+  explicit Predictor(const std::string& device="CPU",
+                     const std::string& track_model_dir="",
+                     const std::string& det_model_dir="",
+                     const std::string& reid_model_dir="",
+                     const double threshold=-1.,
+                     const std::string& run_mode="fluid",
+                     const int gpu_id=0,
+                     const bool use_mkldnn=false,
+                     const int cpu_threads=1,
+                     bool trt_calib_mode=false,
+                     const int min_box_area=200) {
+
+    if (track_model_dir.empty() && det_model_dir.empty()) {
+        throw "Predictor must receive track_model or det_model!";
+    }
+
+    if (!track_model_dir.empty()) {
+      jde_sct_ = std::make_shared<PaddleDetection::JDEPredictor>(device, track_model_dir, threshold, run_mode, gpu_id, use_mkldnn, cpu_threads, trt_calib_mode, min_box_area);
+      use_jde_ = true;
+    }
+    if (!det_model_dir.empty()) {
+      sde_sct_ = std::make_shared<PaddleDetection::SDEPredictor>(device, det_model_dir, reid_model_dir, threshold, run_mode, gpu_id, use_mkldnn, cpu_threads, trt_calib_mode, min_box_area);
+      use_jde_ = false;
+    }
+  }
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+      const double threshold = 0.5,
+      MOTResult* result = nullptr,
+      std::vector<double>* times = nullptr);
+
+ private:
+  std::shared_ptr<PaddleDetection::JDEPredictor> jde_sct_;
+  std::shared_ptr<PaddleDetection::SDEPredictor> sde_sct_;
+  bool use_jde_ = true;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/include/preprocess_op.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <glog/logging.h>
+#include <yaml-cpp/yaml.h>
+
+#include <vector>
+#include <string>
+#include <utility>
+#include <memory>
+#include <unordered_map>
+#include <iostream>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+namespace PaddleDetection {
+
+// Object for storing all preprocessed data
+class ImageBlob {
+ public:
+  // image width and height
+  std::vector<float> im_shape_;
+  // Buffer for image data after preprocessing
+  std::vector<float> im_data_;
+  // in net data shape(after pad)
+  std::vector<float> in_net_shape_;
+  // Evaluation image width and height
+  //std::vector<float>  eval_im_size_f_;
+  // Scale factor for image size to origin image size
+  std::vector<float> scale_factor_;
+};
+
+// Abstraction of preprocessing opration class
+class PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) = 0;
+  virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
+};
+
+class InitInfo : public PreprocessOp{
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class NormalizeImage : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    mean_ = item["mean"].as<std::vector<float>>();
+    scale_ = item["std"].as<std::vector<float>>();
+    is_scale_ = item["is_scale"].as<bool>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  // CHW or HWC
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  bool is_scale_;
+};
+
+class Permute : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+};
+
+class Resize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    interp_ = item["interp"].as<int>();
+    keep_ratio_ = item["keep_ratio"].as<bool>();
+    target_size_ = item["target_size"].as<std::vector<int>>();
+ }
+
+  // Compute best resize scale for x-dimension, y-dimension
+  std::pair<float, float> GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_;
+  bool keep_ratio_;
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+
+class LetterBoxResize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    target_size_ = item["target_size"].as<std::vector<int>>();
+ }
+
+  float GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+// Models with FPN need input shape % stride == 0
+class PadStride : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    stride_ = item["stride"].as<int>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int stride_;
+};
+
+class Preprocessor {
+ public:
+  void Init(const YAML::Node& config_node) {
+    // initialize image info at first
+    ops_["InitInfo"] = std::make_shared<InitInfo>();
+    for (const auto& item : config_node) {
+      auto op_name = item["type"].as<std::string>();
+
+      ops_[op_name] = CreateOp(op_name);
+      ops_[op_name]->Init(item);
+    }
+  }
+
+  std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) {
+    if (name == "Resize") {
+      return std::make_shared<Resize>();
+    } else if (name == "LetterBoxResize") {
+      return std::make_shared<LetterBoxResize>();
+    } else if (name == "Permute") {
+      return std::make_shared<Permute>();
+    } else if (name == "NormalizeImage") {
+      return std::make_shared<NormalizeImage>();
+    } else if (name == "PadStride") {
+      // use PadStride instead of PadBatch
+      return std::make_shared<PadStride>();
+    } 
+    std::cerr << "can not find function of OP: " << name << " and return: nullptr" << std::endl;
+    return nullptr;
+  }
+
+  void Run(cv::Mat* im, ImageBlob* data);
+
+ public:
+  static const std::vector<std::string> RUN_ORDER;
+
+ private:
+  std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
+};
+
+}  // namespace PaddleDetection
+

deploy/pptracking/include/sde_predictor.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <utility>
+#include <ctime>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include "paddle_inference_api.h" // NOLINT
+
+#include "include/preprocess_op.h"
+#include "include/config_parser.h"
+#include "include/utils.h"
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+class SDEPredictor {
+ public:
+  explicit SDEPredictor(const std::string& device,
+                         const std::string& det_model_dir="",
+                         const std::string& reid_model_dir="",
+                         const double threshold=-1.,
+                         const std::string& run_mode="fluid",
+                         const int gpu_id=0,
+                         const bool use_mkldnn=false,
+                         const int cpu_threads=1,
+                         bool trt_calib_mode=false,
+                         const int min_box_area=200) {
+    this->device_ = device;
+    this->gpu_id_ = gpu_id;
+    this->use_mkldnn_ = use_mkldnn;
+    this->cpu_math_library_num_threads_ = cpu_threads;
+    this->trt_calib_mode_ = trt_calib_mode;
+    this->min_box_area_ = min_box_area;
+
+    det_config_.load_config(det_model_dir);
+    this->min_subgraph_size_ = det_config_.min_subgraph_size_;
+    det_preprocessor_.Init(det_config_.preprocess_info_);
+
+    reid_config_.load_config(reid_model_dir);
+    reid_preprocessor_.Init(reid_config_.preprocess_info_);
+
+    LoadModel(det_model_dir, reid_model_dir, run_mode);
+    this->conf_thresh_ = det_config_.conf_thresh_;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(
+    const std::string& det_model_dir,
+    const std::string& reid_model_dir,
+    const std::string& run_mode = "fluid");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs,
+      const double threshold = 0.5,
+      MOTResult* result = nullptr,
+      std::vector<double>* times = nullptr);
+
+ private:
+  std::string device_ = "CPU";
+  float threhold = 0.5;
+  int gpu_id_ = 0;
+  bool use_mkldnn_ = false;
+  int cpu_math_library_num_threads_ = 1;
+  int min_subgraph_size_ = 3;
+  bool trt_calib_mode_ = false;
+
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat& image_mat);
+  // Postprocess result
+  void Postprocess(
+      const cv::Mat dets, const cv::Mat emb,
+      MOTResult* result);
+
+  std::shared_ptr<Predictor> det_predictor_;
+  std::shared_ptr<Predictor> reid_predictor_;
+  Preprocessor det_preprocessor_;
+  Preprocessor reid_preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> bbox_data_;
+  std::vector<float> emb_data_;
+  double threshold_;
+  ConfigPaser det_config_;
+  ConfigPaser reid_config_;
+  float min_box_area_ = 200;
+  float conf_thresh_;
+};
+
+}  // namespace PaddleDetection

deploy/pptracking/include/tracker.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <map>
+#include <vector>
+#include <opencv2/opencv.hpp>
+
+#include "trajectory.h"
+
+namespace PaddleDetection {
+
+typedef std::map<int, int> Match;
+typedef std::map<int, int>::iterator MatchIterator;
+
+struct Track
+{
+    int id;
+    float score;
+    cv::Vec4f ltrb;
+};
+
+class JDETracker
+{
+public:
+    static JDETracker *instance(void);
+    virtual bool update(const cv::Mat &dets, const cv::Mat &emb, std::vector<Track> &tracks);
+private:
+    JDETracker(void);
+    virtual ~JDETracker(void) {}
+    cv::Mat motion_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+    void linear_assignment(const cv::Mat &cost, float cost_limit, Match &matches,
+        std::vector<int> &mismatch_row, std::vector<int> &mismatch_col);
+    void remove_duplicate_trajectory(TrajectoryPool &a, TrajectoryPool &b, float iou_thresh=0.15f);
+private:
+    static JDETracker *me;
+    int timestamp;
+    TrajectoryPool tracked_trajectories;
+    TrajectoryPool lost_trajectories;
+    TrajectoryPool removed_trajectories;
+    int max_lost_time;
+    float lambda;
+    float det_thresh;
+};
+
+} // namespace PaddleDetection

deploy/pptracking/include/trajectory.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <vector>
+#include <opencv2/opencv.hpp>
+
+namespace PaddleDetection {
+
+typedef enum
+{
+    New = 0,
+    Tracked = 1,
+    Lost = 2,
+    Removed = 3
+} TrajectoryState;
+
+class Trajectory;
+typedef std::vector<Trajectory> TrajectoryPool;
+typedef std::vector<Trajectory>::iterator TrajectoryPoolIterator;
+typedef std::vector<Trajectory *>TrajectoryPtrPool;
+typedef std::vector<Trajectory *>::iterator TrajectoryPtrPoolIterator;
+
+class TKalmanFilter : public cv::KalmanFilter
+{
+public:
+    TKalmanFilter(void);
+    virtual ~TKalmanFilter(void) {}
+    virtual void init(const cv::Mat &measurement);
+    virtual const cv::Mat &predict();
+    virtual const cv::Mat &correct(const cv::Mat &measurement);
+    virtual void project(cv::Mat &mean, cv::Mat &covariance) const;
+private:
+    float std_weight_position;
+    float std_weight_velocity;
+};
+
+inline TKalmanFilter::TKalmanFilter(void) : cv::KalmanFilter(8, 4)
+{
+    cv::KalmanFilter::transitionMatrix = cv::Mat::eye(8, 8, CV_32F);
+    for (int i = 0; i < 4; ++i)
+        cv::KalmanFilter::transitionMatrix.at<float>(i, i + 4) = 1;
+    cv::KalmanFilter::measurementMatrix = cv::Mat::eye(4, 8, CV_32F);
+    std_weight_position = 1/20.f;
+    std_weight_velocity = 1/160.f;
+}
+
+class Trajectory : public TKalmanFilter
+{
+public:
+    Trajectory();
+    Trajectory(cv::Vec4f &ltrb, float score, const cv::Mat &embedding);
+    Trajectory(const Trajectory &other);
+    Trajectory &operator=(const Trajectory &rhs);
+    virtual ~Trajectory(void) {};
+
+    static int next_id();
+    virtual const cv::Mat &predict(void);
+    virtual void update(Trajectory &traj, int timestamp, bool update_embedding=true);
+    virtual void activate(int timestamp);
+    virtual void reactivate(Trajectory &traj, int timestamp, bool newid=false);
+    virtual void mark_lost(void);
+    virtual void mark_removed(void);
+
+    friend TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPool &operator+=(TrajectoryPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPool operator-(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPool &operator-=(TrajectoryPool &a, const TrajectoryPool &b);
+    friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, TrajectoryPool &b);
+    friend TrajectoryPtrPool operator-(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+
+    friend cv::Mat embedding_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+
+    friend cv::Mat mahalanobis_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+
+    friend cv::Mat iou_distance(const TrajectoryPool &a, const TrajectoryPool &b);
+    friend cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b);
+    friend cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b);
+private:   
+    void update_embedding(const cv::Mat &embedding);
+public:
+    TrajectoryState state;
+    cv::Vec4f ltrb;
+    cv::Mat smooth_embedding;
+    int id;
+    bool is_activated;
+    int timestamp;
+    int starttime;
+    float score;
+private:
+    static int count;
+    cv::Vec4f xyah;
+    cv::Mat current_embedding;
+    float eta;
+    int length;
+};
+
+inline cv::Vec4f ltrb2xyah(cv::Vec4f &ltrb)
+{
+    cv::Vec4f xyah;
+    xyah[0] = (ltrb[0] + ltrb[2]) * 0.5f;
+    xyah[1] = (ltrb[1] + ltrb[3]) * 0.5f;
+    xyah[3] =  ltrb[3] - ltrb[1];
+    xyah[2] = (ltrb[2] - ltrb[0]) / xyah[3];
+    return xyah;
+}
+
+inline Trajectory::Trajectory() :
+    state(New), ltrb(cv::Vec4f()), smooth_embedding(cv::Mat()), id(0),
+    is_activated(false), timestamp(0), starttime(0), score(0), eta(0.9), length(0)
+{
+}
+
+inline Trajectory::Trajectory(cv::Vec4f &ltrb_, float score_, const cv::Mat &embedding) :
+    state(New), ltrb(ltrb_), smooth_embedding(cv::Mat()), id(0),
+    is_activated(false), timestamp(0), starttime(0), score(score_), eta(0.9), length(0)
+{
+    xyah = ltrb2xyah(ltrb);
+    update_embedding(embedding);
+}
+
+inline Trajectory::Trajectory(const Trajectory &other):
+    state(other.state), ltrb(other.ltrb), id(other.id), is_activated(other.is_activated),
+    timestamp(other.timestamp), starttime(other.starttime), xyah(other.xyah),
+    score(other.score), eta(other.eta), length(other.length)
+{    
+    other.smooth_embedding.copyTo(smooth_embedding);
+    other.current_embedding.copyTo(current_embedding);
+    // copy state in KalmanFilter
+    
+    other.statePre.copyTo(cv::KalmanFilter::statePre);
+    other.statePost.copyTo(cv::KalmanFilter::statePost);
+    other.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+    other.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+    
+}
+
+inline Trajectory &Trajectory::operator=(const Trajectory &rhs)
+{
+    this->state = rhs.state;
+    this->ltrb = rhs.ltrb;
+    rhs.smooth_embedding.copyTo(this->smooth_embedding);
+    this->id = rhs.id;
+    this->is_activated = rhs.is_activated;
+    this->timestamp = rhs.timestamp;
+    this->starttime = rhs.starttime; 
+    this->xyah = rhs.xyah;
+    this->score = rhs.score;
+    rhs.current_embedding.copyTo(this->current_embedding);            
+    this->eta = rhs.eta;    
+    this->length = rhs.length;
+
+    // copy state in KalmanFilter
+    
+    rhs.statePre.copyTo(cv::KalmanFilter::statePre);
+    rhs.statePost.copyTo(cv::KalmanFilter::statePost);
+    rhs.errorCovPre.copyTo(cv::KalmanFilter::errorCovPre);
+    rhs.errorCovPost.copyTo(cv::KalmanFilter::errorCovPost);
+    
+    return *this;
+}
+
+inline int Trajectory::next_id()
+{
+    ++count;
+    return count;
+}
+
+inline void Trajectory::mark_lost(void)
+{
+    state = Lost;
+}
+
+inline void Trajectory::mark_removed(void)
+{
+    state = Removed;
+}
+
+}   // namespace PaddleDetection 

deploy/pptracking/include/utils.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include <utility>
+#include <ctime>
+#include <numeric>
+#include <algorithm>
+
+#include "include/tracker.h"
+
+namespace PaddleDetection {
+
+struct Rect
+{
+    float left;
+    float top;
+    float right;
+    float bottom;
+};
+
+struct MOTTrack
+{
+    int ids;
+    float score;
+    Rect rects;
+    int class_id = -1;
+};
+
+typedef std::vector<MOTTrack> MOTResult;
+
+}  // namespace PaddleDetection

deploy/pptracking/scripts/build.sh

+# 是否使用GPU(即是否使用 CUDA)
+WITH_GPU=OFF
+
+# 是否使用MKL or openblas，TX2需要设置为OFF
+WITH_MKL=ON
+
+# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
+WITH_TENSORRT=OFF
+
+# paddle 预测库lib名称，由于不同平台不同版本预测库lib名称不同，请查看所下载的预测库中`paddle_inference/lib/`文件夹下`lib`的名称
+PADDLE_LIB_NAME=libpaddle_inference
+
+# TensorRT 的include路径
+TENSORRT_INC_DIR=/path/to/tensorrt/include
+
+# TensorRT 的lib路径
+TENSORRT_LIB_DIR=/path/to/tensorrt/lib
+
+# Paddle 预测库路径
+PADDLE_DIR=/path/to/paddle_inference
+
+# CUDA 的 lib 路径
+CUDA_LIB=/path/to/cuda/lib
+
+# CUDNN 的 lib 路径
+CUDNN_LIB=/path/to/cudnn/lib
+
+MACHINE_TYPE=`uname -m`
+echo "MACHINE_TYPE: "${MACHINE_TYPE}
+
+
+if [ "$MACHINE_TYPE" = "x86_64" ]
+then
+  echo "set OPENCV_DIR for x86_64"
+  # linux系统通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://paddledet.bj.bcebos.com/data/opencv-3.4.16_gcc8.2_ffmpeg.tar.gz
+  tar -xvf opencv-3.4.16_gcc8.2_ffmpeg.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/opencv-3.4.16_gcc8.2_ffmpeg
+
+elif [ "$MACHINE_TYPE" = "aarch64" ]
+then
+  echo "set OPENCV_DIR for aarch64"
+  # TX2平台通过以下命令下载预编译的opencv
+  mkdir -p $(pwd)/deps && cd $(pwd)/deps
+  wget -c https://bj.bcebos.com/v1/paddledet/data/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz
+  tar -xvf TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0.tar.gz && cd ..
+
+  # set OPENCV_DIR
+  OPENCV_DIR=$(pwd)/deps/TX2_JetPack4.3_opencv_3.4.6_gcc7.5.0/
+
+else
+  echo "Please set OPENCV_DIR manually"
+fi
+
+echo "OPENCV_DIR: "$OPENCV_DIR
+
+# 以下无需改动
+rm -rf build
+mkdir -p build
+cd build
+cmake .. \
+    -DWITH_GPU=${WITH_GPU} \
+    -DWITH_MKL=${WITH_MKL} \
+    -DWITH_TENSORRT=${WITH_TENSORRT} \
+    -DTENSORRT_LIB_DIR=${TENSORRT_LIB_DIR} \
+    -DTENSORRT_INC_DIR=${TENSORRT_INC_DIR} \
+    -DPADDLE_DIR=${PADDLE_DIR} \
+    -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
+    -DCUDA_LIB=${CUDA_LIB} \
+    -DCUDNN_LIB=${CUDNN_LIB} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DPADDLE_LIB_NAME=${PADDLE_LIB_NAME} \
+
+make
+echo "make finished!"

deploy/pptracking/src/jde_predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <iomanip>
+#include <chrono>
+#include "include/jde_predictor.h"
+
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void JDEPredictor::LoadModel(const std::string& model_dir,
+                               const std::string& run_mode) {
+  paddle_infer::Config config;
+  std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
+  std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
+  config.SetModel(prog_file, params_file);
+  if (this->device_ == "GPU") {
+    config.EnableUseGpu(200, this->gpu_id_);
+    config.SwitchIrOptim(true);
+    // use tensorrt
+    if (run_mode != "fluid") {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (run_mode == "trt_fp32") {
+        precision = paddle_infer::Config::Precision::kFloat32;
+      }
+      else if (run_mode == "trt_fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      }
+      else if (run_mode == "trt_int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      } else {
+          printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'");
+      }
+      // set tensorrt
+      config.EnableTensorRtEngine(
+          1 << 30,
+          1,
+          this->min_subgraph_size_,
+          precision,
+          false,
+          this->trt_calib_mode_);
+    }
+  } else if (this->device_ == "XPU"){
+    config.EnableXpu(10*1024*1024);
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+  config.SwitchUseFeedFetchOps(false);
+  config.SwitchIrOptim(true);
+  config.DisableGlogInfo();
+  // Memory optimization
+  config.EnableMemoryOptim();
+  predictor_ = std::move(CreatePredictor(config));
+}
+
+void FilterDets(const float conf_thresh, const cv::Mat dets, std::vector<int>* index) {
+  for (int i = 0; i < dets.rows; ++i) {
+    float score = *dets.ptr<float>(i, 4);
+    if (score > conf_thresh) {
+      index->push_back(i);
+    }
+  }
+}
+
+void JDEPredictor::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void JDEPredictor::Postprocess(
+    const cv::Mat dets, const cv::Mat emb,
+    MOTResult* result) {
+  result->clear();
+  std::vector<Track> tracks;
+  std::vector<int> valid;
+  FilterDets(conf_thresh_, dets, &valid);
+  cv::Mat new_dets, new_emb;
+  for (int i = 0; i < valid.size(); ++i) {
+    new_dets.push_back(dets.row(valid[i]));
+    new_emb.push_back(emb.row(valid[i]));
+  }
+  JDETracker::instance()->update(new_dets, new_emb, tracks);
+  if (tracks.size() == 0) {
+    MOTTrack mot_track;
+    Rect ret = {*dets.ptr<float>(0, 0), 
+                    *dets.ptr<float>(0, 1),
+                    *dets.ptr<float>(0, 2),
+                    *dets.ptr<float>(0, 3)};
+    mot_track.ids = 1;
+    mot_track.score = *dets.ptr<float>(0, 4);
+    mot_track.rects = ret;
+    result->push_back(mot_track);
+  } else {
+    std::vector<Track>::iterator titer;
+    for (titer = tracks.begin(); titer != tracks.end(); ++titer) {
+      if (titer->score < threshold_) {
+        continue;
+      } else {
+        float w = titer->ltrb[2] - titer->ltrb[0];
+        float h = titer->ltrb[3] - titer->ltrb[1];
+        bool vertical = w / h > 1.6;
+        float area = w * h;
+        if (area > min_box_area_ && !vertical) {
+          MOTTrack mot_track;
+          Rect ret = {titer->ltrb[0],
+                          titer->ltrb[1],
+                          titer->ltrb[2],
+                          titer->ltrb[3]};
+          mot_track.rects = ret;
+          mot_track.score = titer->score;
+          mot_track.ids = titer->id;
+          result->push_back(mot_track);
+        }
+      }
+    } 
+  }
+}
+
+void JDEPredictor::Predict(const std::vector<cv::Mat> imgs,
+      const double threshold,
+      MOTResult* result,
+      std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+  
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    // TODO: reduce cost time
+    in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+
+  // Prepare input tensor
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputHandle(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Reshape({batch_size, 3, rh, rw});
+      in_tensor->CopyFromCpu(in_data_all.data());
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(im_shape_all.data());
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Reshape({batch_size, 2});
+      in_tensor->CopyFromCpu(scale_factor_all.data());
+    }
+  }
+  
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<int> bbox_shape;
+  std::vector<int> emb_shape;
+
+  // Run predictor
+  auto inference_start = std::chrono::steady_clock::now();
+  predictor_->Run();
+  // Get output tensor
+  auto output_names = predictor_->GetOutputNames();
+  auto bbox_tensor = predictor_->GetOutputHandle(output_names[0]);
+  bbox_shape = bbox_tensor->shape();
+  auto emb_tensor = predictor_->GetOutputHandle(output_names[1]);
+  emb_shape = emb_tensor->shape();
+  // Calculate bbox length
+  int bbox_size = 1;
+  for (int j = 0; j < bbox_shape.size(); ++j) {
+    bbox_size *= bbox_shape[j];
+  }
+  // Calculate emb length
+  int emb_size = 1;
+  for (int j = 0; j < emb_shape.size(); ++j) {
+    emb_size *= emb_shape[j];
+  }
+
+  bbox_data_.resize(bbox_size);
+  bbox_tensor->CopyToCpu(bbox_data_.data()); 
+
+  emb_data_.resize(emb_size);
+  emb_tensor->CopyToCpu(emb_data_.data());
+  auto inference_end = std::chrono::steady_clock::now();
+
+  // Postprocessing result
+  auto postprocess_start = std::chrono::steady_clock::now();
+  result->clear();
+
+  cv::Mat dets(bbox_shape[0], 6, CV_32FC1, bbox_data_.data());
+  cv::Mat emb(bbox_shape[0], emb_shape[1], CV_32FC1, emb_data_.data());
+
+  Postprocess(dets, emb, result);
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  (*times)[0] += double(preprocess_diff.count() * 1000);
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  (*times)[1] += double(inference_diff.count() * 1000);
+  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  (*times)[2] += double(postprocess_diff.count() * 1000);
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/src/lapjv.cpp

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "include/lapjv.h"
+
+namespace PaddleDetection {
+
+/** Column-reduction and reduction transfer for a dense cost matrix.
+ */
+int _ccrrt_dense(const int n, float *cost[],
+                     int *free_rows, int *x, int *y, float *v)
+{
+    int n_free_rows;
+    bool *unique;
+
+    for (int i = 0; i < n; i++) {
+        x[i] = -1;
+        v[i] = LARGE;
+        y[i] = 0;
+    }
+    for (int i = 0; i < n; i++) {
+        for (int j = 0; j < n; j++) {
+            const float c = cost[i][j];
+            if (c < v[j]) {
+                v[j] = c;
+                y[j] = i;
+            }
+        }
+    }
+    NEW(unique, bool, n);
+    memset(unique, TRUE, n);
+    {
+        int j = n;
+        do {
+            j--;
+            const int i = y[j];
+            if (x[i] < 0) {
+                x[i] = j;
+            } else {
+                unique[i] = FALSE;
+                y[j] = -1;
+            }
+        } while (j > 0);
+    }
+    n_free_rows = 0;
+    for (int i = 0; i < n; i++) {
+        if (x[i] < 0) {
+            free_rows[n_free_rows++] = i;
+        } else if (unique[i]) {
+            const int j = x[i];
+            float min = LARGE;
+            for (int j2 = 0; j2 < n; j2++) {
+                if (j2 == (int)j) {
+                    continue;
+                }
+                const float c = cost[i][j2] - v[j2];
+                if (c < min) {
+                    min = c;
+                }
+            }
+            v[j] -= min;
+        }
+    }
+    FREE(unique);
+    return n_free_rows;
+}
+
+
+/** Augmenting row reduction for a dense cost matrix.
+ */
+int _carr_dense(
+    const int n, float *cost[],
+    const int n_free_rows,
+    int *free_rows, int *x, int *y, float *v)
+{
+    int current = 0;
+    int new_free_rows = 0;
+    int rr_cnt = 0;
+    while (current < n_free_rows) {
+        int i0;
+        int j1, j2;
+        float v1, v2, v1_new;
+        bool v1_lowers;
+
+        rr_cnt++;
+        const int free_i = free_rows[current++];
+        j1 = 0;
+        v1 = cost[free_i][0] - v[0];
+        j2 = -1;
+        v2 = LARGE;
+        for (int j = 1; j < n; j++) {
+            const float c = cost[free_i][j] - v[j];
+            if (c < v2) {
+                if (c >= v1) {
+                    v2 = c;
+                    j2 = j;
+                } else {
+                    v2 = v1;
+                    v1 = c;
+                    j2 = j1;
+                    j1 = j;
+                }
+            }
+        }
+        i0 = y[j1];
+        v1_new = v[j1] - (v2 - v1);
+        v1_lowers = v1_new < v[j1];
+        if (rr_cnt < current * n) {
+            if (v1_lowers) {
+                v[j1] = v1_new;
+            } else if (i0 >= 0 && j2 >= 0) {
+                j1 = j2;
+                i0 = y[j2];
+            }
+            if (i0 >= 0) {
+                if (v1_lowers) {
+                    free_rows[--current] = i0;
+                } else {
+                    free_rows[new_free_rows++] = i0;
+                }
+            }
+        } else {
+            if (i0 >= 0) {
+                free_rows[new_free_rows++] = i0;
+            }
+        }
+        x[free_i] = j1;
+        y[j1] = free_i;
+    }
+    return new_free_rows;
+}
+
+
+/** Find columns with minimum d[j] and put them on the SCAN list.
+ */
+int _find_dense(const int n, int lo, float *d, int *cols, int *y)
+{
+    int hi = lo + 1;
+    float mind = d[cols[lo]];
+    for (int k = hi; k < n; k++) {
+        int j = cols[k];
+        if (d[j] <= mind) {
+            if (d[j] < mind) {
+                hi = lo;
+                mind = d[j];
+            }
+            cols[k] = cols[hi];
+            cols[hi++] = j;
+        }
+    }
+    return hi;
+}
+
+
+// Scan all columns in TODO starting from arbitrary column in SCAN
+// and try to decrease d of the TODO columns using the SCAN column.
+int _scan_dense(const int n, float *cost[],
+                    int *plo, int*phi,
+                    float *d, int *cols, int *pred,
+                    int *y, float *v)
+{
+    int lo = *plo;
+    int hi = *phi;
+    float h, cred_ij;
+
+    while (lo != hi) {
+        int j = cols[lo++];
+        const int i = y[j];
+        const float mind = d[j];
+        h = cost[i][j] - v[j] - mind;
+        // For all columns in TODO
+        for (int k = hi; k < n; k++) {
+            j = cols[k];
+            cred_ij = cost[i][j] - v[j] - h;
+            if (cred_ij < d[j]) {
+                d[j] = cred_ij;
+                pred[j] = i;
+                if (cred_ij == mind) {
+                    if (y[j] < 0) {
+                        return j;
+                    }
+                    cols[k] = cols[hi];
+                    cols[hi++] = j;
+                }
+            }
+        }
+    }
+    *plo = lo;
+    *phi = hi;
+    return -1;
+}
+
+
+/** Single iteration of modified Dijkstra shortest path algorithm as explained in the JV paper.
+ *
+ * This is a dense matrix version.
+ *
+ * \return The closest free column index.
+ */
+int find_path_dense(
+    const int n, float *cost[],
+    const int start_i,
+    int *y, float *v,
+    int *pred)
+{
+    int lo = 0, hi = 0;
+    int final_j = -1;
+    int n_ready = 0;
+    int *cols;
+    float *d;
+
+    NEW(cols, int, n);
+    NEW(d, float, n);
+
+    for (int i = 0; i < n; i++) {
+        cols[i] = i;
+        pred[i] = start_i;
+        d[i] = cost[start_i][i] - v[i];
+    }
+    while (final_j == -1) {
+        // No columns left on the SCAN list.
+        if (lo == hi) {
+            n_ready = lo;
+            hi = _find_dense(n, lo, d, cols, y);
+            for (int k = lo; k < hi; k++) {
+                const int j = cols[k];
+                if (y[j] < 0) {
+                    final_j = j;
+                }
+            }
+        }
+        if (final_j == -1) {
+            final_j = _scan_dense(
+                    n, cost, &lo, &hi, d, cols, pred, y, v);
+        }
+    }
+
+    {
+        const float mind = d[cols[lo]];
+        for (int k = 0; k < n_ready; k++) {
+            const int j = cols[k];
+            v[j] += d[j] - mind;
+        }
+    }
+
+    FREE(cols);
+    FREE(d);
+
+    return final_j;
+}
+
+
+/** Augment for a dense cost matrix.
+ */
+int _ca_dense(
+    const int n, float *cost[],
+    const int n_free_rows,
+    int *free_rows, int *x, int *y, float *v)
+{
+    int *pred;
+
+    NEW(pred, int, n);
+
+    for (int *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {
+        int i = -1, j;
+        int k = 0;
+
+        j = find_path_dense(n, cost, *pfree_i, y, v, pred);
+        while (i != *pfree_i) {
+            i = pred[j];
+            y[j] = i;
+            SWAP_INDICES(j, x[i]);
+            k++;
+        }
+    }
+    FREE(pred);
+    return 0;
+}
+
+
+/** Solve dense sparse LAP.
+ */
+int lapjv_internal(
+    const cv::Mat &cost, const bool extend_cost, const float cost_limit,
+    int *x, int *y ) {
+    int n_rows = cost.rows;
+    int n_cols = cost.cols;
+    int n;
+    if (n_rows == n_cols) {
+      n = n_rows;
+    } else if (!extend_cost) {
+      throw std::invalid_argument("Square cost array expected. If cost is intentionally non-square, pass extend_cost=True.");
+    }
+
+    // Get extend cost
+    if (extend_cost || cost_limit < LARGE) {
+      n = n_rows + n_cols;
+    }
+    cv::Mat cost_expand(n, n, CV_32F);
+    float expand_value;
+    if (cost_limit < LARGE) {
+      expand_value = cost_limit / 2;
+    } else {
+      double max_v;
+      minMaxLoc(cost, nullptr, &max_v);
+      expand_value = (float)max_v + 1;
+    }
+
+    for (int i = 0; i < n; ++i) {
+      for (int j = 0; j < n; ++j) {
+        cost_expand.at<float>(i, j) = expand_value;
+        if (i >= n_rows && j >= n_cols) {
+          cost_expand.at<float>(i, j) = 0;
+        } else if (i < n_rows && j < n_cols) {
+          cost_expand.at<float>(i, j) = cost.at<float>(i, j);
+        }
+      }
+    } 
+
+    // Convert Mat to pointer array
+    float **cost_ptr;
+    NEW(cost_ptr, float *, n);
+    for (int i = 0; i < n; ++i) {
+      NEW(cost_ptr[i], float, n);
+    }
+    for (int i = 0; i < n; ++i) {
+      for (int j = 0; j < n; ++j) {
+        cost_ptr[i][j] = cost_expand.at<float>(i, j);
+      }
+    }
+
+    int ret;
+    int *free_rows;
+    float *v;
+    int *x_c;
+    int *y_c;
+
+    NEW(free_rows, int, n);
+    NEW(v, float, n);
+    NEW(x_c, int, n);
+    NEW(y_c, int, n);
+
+    ret = _ccrrt_dense(n, cost_ptr, free_rows, x_c, y_c, v);
+    int i = 0;
+    while (ret > 0 && i < 2) {
+      ret = _carr_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+      i++;
+    }
+    if (ret > 0) {
+      ret = _ca_dense(n, cost_ptr, ret, free_rows, x_c, y_c, v);
+    }
+    FREE(v);
+    FREE(free_rows);
+    for (int i = 0; i < n; ++i) {
+      FREE(cost_ptr[i]);
+    }
+    FREE(cost_ptr);
+    if (ret != 0) {
+      if (ret == -1){
+        throw "Out of memory.";
+      }
+      throw "Unknown error (lapjv_internal)";
+    }
+    // Get output of x, y, opt
+    for (int i = 0; i < n; ++i) {
+      if (i < n_rows) {
+        x[i] = x_c[i];
+        if (x[i] >= n_cols) {
+          x[i] = -1;
+        }
+      }      
+      if (i < n_cols) {
+        y[i] = y_c[i];
+        if (y[i] >= n_rows) {
+          y[i] = -1;
+        }
+      } 
+    }
+    
+    FREE(x_c);
+    FREE(y_c);
+    return ret;
+}
+
+} // namespace PaddleDetection

deploy/pptracking/src/main.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <glog/logging.h>
+
+#include <iostream>
+#include <string>
+#include <vector>
+#include <numeric>
+#include <sys/types.h>
+#include <sys/stat.h>
+#include <math.h>
+#include <algorithm>
+
+#ifdef _WIN32
+#include <direct.h>
+#include <io.h>
+#elif LINUX
+#include <stdarg.h>
+#include <sys/stat.h>
+#endif
+
+#include "include/pipeline.h"
+#include <gflags/gflags.h>
+
+
+DEFINE_string(video_file, "", "Path of input video.");
+DEFINE_string(video_other_file, "", "Path of other input video used for MCT.");
+DEFINE_string(device, "CPU", "Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU.");
+DEFINE_double(threshold, 0.5, "Threshold of score.");
+DEFINE_string(output_dir, "output", "Directory of output visualization files.");
+DEFINE_string(run_mode, "fluid", "Mode of running(fluid/trt_fp32/trt_fp16/trt_int8)");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
+DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
+DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
+DEFINE_bool(trt_calib_mode, false, "If the model is produced by TRT offline quantitative calibration, trt_calib_mode need to set True");
+DEFINE_bool(tiny_obj, false, "Whether tracking tiny object");
+DEFINE_bool(count, false, "Whether counting after tracking");
+DEFINE_bool(save_result, false, "Whether saving result after tracking");
+DEFINE_string(scene, "", "scene of tracking system, it can be : pedestrian/vehicle/multiclass");
+DEFINE_bool(is_mct, false, "Whether use multi-camera tracking");
+
+static std::string DirName(const std::string &filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path){
+#ifdef _WIN32
+  struct _stat buffer;
+  return (_stat(path.c_str(), &buffer) == 0);
+#else
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+#endif  // !_WIN32
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+#ifdef _WIN32
+  ret = _mkdir(path.c_str());
+#else
+  ret = mkdir(path.c_str(), 0755);
+#endif  // !_WIN32
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+int main(int argc, char** argv) {
+  // Parsing command-line
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  if (FLAGS_video_file.empty() || FLAGS_scene.empty()) {
+    std::cout << "Usage: ./main "
+              << "-video_file=/PATH/TO/INPUT/IMAGE/ "
+              << "-scene=pedestrian/vehicle/multiclass" << std::endl;
+    return -1;
+  }
+  if (!(FLAGS_run_mode == "fluid" || FLAGS_run_mode == "trt_fp32"
+      || FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
+    std::cout << "run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
+    return -1;
+  }
+  transform(FLAGS_device.begin(),FLAGS_device.end(),FLAGS_device.begin(),::toupper);
+  if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" || FLAGS_device == "XPU")) {
+    std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
+    return -1;
+  }
+
+  PaddleDetection::Pipeline pipeline(
+                    FLAGS_device, FLAGS_threshold, 
+                    FLAGS_output_dir, FLAGS_run_mode, FLAGS_gpu_id, 
+                    FLAGS_use_mkldnn, FLAGS_cpu_threads,
+                    FLAGS_count, FLAGS_save_result);
+
+  pipeline.SelectModel(FLAGS_scene, FLAGS_tiny_obj, FLAGS_is_mct);
+  pipeline.SetInput(FLAGS_video_file);
+  if (!FLAGS_video_other_file.empty()) {
+    pipeline.SetInput(FLAGS_video_other_file);
+  }
+  pipeline.Run();
+  return 0;
+}

deploy/pptracking/src/pipeline.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <sstream>
+// for setprecision
+#include <iostream>
+#include <string>
+#include <iomanip>
+#include <chrono>
+#include "include/pipeline.h"
+#include "include/postprocess.h"
+#include "include/predictor.h"
+
+namespace PaddleDetection {
+
+
+void Pipeline::SetInput(std::string& input_video) {
+  input_.push_back(input_video);
+}
+
+void Pipeline::SelectModel(const std::string& scene,
+                           const bool tiny_obj,
+                           const bool is_mct) {
+  // Single camera model
+  // use deepsort for multiclass tracking
+  // use fairmot for single class tracking
+  if (scene == "pedestrian") {
+      track_model_dir_ = "../pedestrian_track";
+  } else if (scene != "vehicle") {
+      track_model_dir_ = "../vehicle_track";
+  } else if (scene == "multiclass") {
+      det_model_dir_ = "../multiclass_det";
+      reid_model_dir_ = "../multiclass_reid";
+  }
+
+  // Multi-camera model
+  if (is_mct && scene == "pedestrian") {
+      mct_model_dir_ = "../pedestrian_mct";
+  } else if (is_mct && scene == "vehicle") {
+      mct_model_dir_ = "../vehicle_mct";
+  } else if (is_mct && scene == "multiclass") {
+      throw "Multi-camera tracking is not supported in multiclass scene now.";
+  } 
+}
+
+void Pipeline::Run() {
+
+  if (track_model_dir_.empty()) {
+    std::cout << "Pipeline must use SelectModel before Run";
+    return;
+  }
+  if (input_.size() == 0) {
+    std::cout << "Pipeline must use SetInput before Run";
+    return;
+  }
+
+  if (mct_model_dir_.empty()) {
+    // single camera
+    if (input_.size() > 1) {
+      throw "Single camera tracking except single video, but received %d", input_.size();
+    }
+    PredictSCT(input_[0]);
+  } else {
+    // multi cameras
+    if (input_.size() != 2) {
+      throw "Multi camera tracking except two videos, but received %d", input_.size();
+    }
+    PredictMCT(input_);
+  }
+}
+
+void Pipeline::PredictSCT(const std::string& video_path) {
+
+  PaddleDetection::Predictor sct(device_, track_model_dir_, det_model_dir_, reid_model_dir_, threshold_, run_mode_, gpu_id_, use_mkldnn_, cpu_threads_, trt_calib_mode_);
+  // Open video
+  cv::VideoCapture capture;
+  capture.open(video_path.c_str());
+  if (!capture.isOpened()) {
+    printf("can not open video : %s\n", video_path.c_str());
+    return;
+  }
+
+  // Get Video info : resolution, fps
+  int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
+  int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
+  int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
+
+  // Create VideoWriter for output
+  cv::VideoWriter video_out;
+  std::string video_out_path = "mot_output.mp4";
+  int fcc = cv::VideoWriter::fourcc('m','p','4','v');
+  video_out.open(video_out_path.c_str(),
+                 fcc, //0x00000021,
+                 video_fps,
+                 cv::Size(video_width, video_height),
+                 true);
+  if (!video_out.isOpened()) {
+    printf("create video writer failed!\n");
+    return;
+  }
+
+  PaddleDetection::MOTResult result;
+  std::vector<double> det_times(3);
+  std::vector<int> count_list;
+  std::vector<int> in_count_list;
+  std::vector<int> out_count_list;
+  double times;
+  // Capture all frames and do inference
+  cv::Mat frame;
+  int frame_id = 0;
+  while (capture.read(frame)) {
+    if (frame.empty()) {
+      break;
+    }
+    std::vector<cv::Mat> imgs;
+    imgs.push_back(frame);
+    printf("frame_id: %d\n", frame_id);
+    sct.Predict(imgs, threshold_, &result, &det_times);
+    frame_id += 1;
+    times = std::accumulate(det_times.begin(), det_times.end(), 0) / frame_id;
+
+    cv::Mat out_im = PaddleDetection::VisualizeTrackResult(
+        frame, result, 1000./times, frame_id);
+    
+    if (count_) {
+      // Count total number 
+      // Count in & out number
+      PaddleDetection::FlowStatistic(result, frame_id, &count_list, &in_count_list, &out_count_list);
+    }
+    if (save_result_) {
+      PaddleDetection::SaveResult(result, output_dir_);
+    }
+    video_out.write(out_im);
+  }
+  capture.release();
+  video_out.release();
+  PrintBenchmarkLog(det_times, frame_id);
+  printf("Visualized output saved as %s\n", video_out_path.c_str());
+}
+
+void Pipeline::PredictMCT(const std::vector<std::string> video_path) {
+  throw "Not Implement!";
+}
+
+void Pipeline::PrintBenchmarkLog(std::vector<double> det_time, int img_num){
+  LOG(INFO) << "----------------------- Config info -----------------------";
+  LOG(INFO) << "runtime_device: " << device_;
+  LOG(INFO) << "ir_optim: " << "True";
+  LOG(INFO) << "enable_memory_optim: " << "True";
+  int has_trt = run_mode_.find("trt");
+  if (has_trt >= 0) {
+    LOG(INFO) << "enable_tensorrt: " << "True";
+    std::string precision = run_mode_.substr(4, 8);
+    LOG(INFO) << "precision: " << precision;
+  } else {
+    LOG(INFO) << "enable_tensorrt: " << "False";
+    LOG(INFO) << "precision: " << "fp32";
+  }
+  LOG(INFO) << "enable_mkldnn: " << (use_mkldnn_ ? "True" : "False");
+  LOG(INFO) << "cpu_math_library_num_threads: " << cpu_threads_;
+  LOG(INFO) << "----------------------- Perf info ------------------------";
+  LOG(INFO) << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.);
+  img_num = std::max(1, img_num);
+  LOG(INFO) << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num;
+}
+
+
+} // namespace PaddleDetection
+

deploy/pptracking/src/postprocess.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <sstream>
+// for setprecision
+#include <iomanip>
+#include <chrono>
+#include "include/postprocess.h"
+
+namespace PaddleDetection {
+
+cv::Scalar GetColor(int idx) {
+  idx = idx * 3;
+  cv::Scalar color = cv::Scalar((37 * idx) % 255, 
+                                (17 * idx) % 255, 
+                                (29 * idx) % 255);
+  return color;
+}
+
+cv::Mat VisualizeTrackResult(const cv::Mat& img,
+                        const MOTResult& results,
+                        const float fps, const int frame_id) {
+  cv::Mat vis_img = img.clone();
+  int im_h = img.rows;
+  int im_w = img.cols;
+  float text_scale = std::max(1, int(im_w / 1600.));
+  float text_thickness = 2.;
+  float line_thickness = std::max(1, int(im_w / 500.));
+
+  std::ostringstream oss;
+  oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+  oss << "frame: " << frame_id<<" ";
+  oss << "fps: " << fps<<" ";
+  oss << "num: " << results.size();
+  std::string text = oss.str();
+
+  cv::Point origin;
+  origin.x = 0;
+  origin.y = int(15 * text_scale);
+  cv::putText(
+        vis_img,
+        text,
+        origin,
+        cv::FONT_HERSHEY_PLAIN,
+        text_scale, (0, 0, 255), 2);
+
+  for (int i = 0; i < results.size(); ++i) {
+    const int obj_id = results[i].ids;
+    const float score = results[i].score;
+    
+    cv::Scalar color = GetColor(obj_id);
+
+    cv::Point pt1 = cv::Point(results[i].rects.left, results[i].rects.top);
+    cv::Point pt2 = cv::Point(results[i].rects.right, results[i].rects.bottom);
+    cv::Point id_pt = cv::Point(results[i].rects.left, results[i].rects.top + 10);
+    cv::Point score_pt = cv::Point(results[i].rects.left, results[i].rects.top - 10);
+    cv::rectangle(vis_img, pt1, pt2, color, line_thickness);
+
+    std::ostringstream idoss;
+    idoss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    idoss << obj_id;
+    std::string id_text = idoss.str();
+
+    cv::putText(vis_img,
+                id_text,
+                id_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+
+    std::ostringstream soss;
+    soss << std::setiosflags(std::ios::fixed) << std::setprecision(2);
+    soss << score;
+    std::string score_text = soss.str();
+
+    cv::putText(vis_img,
+                score_text,
+                score_pt,
+                cv::FONT_HERSHEY_PLAIN,
+                text_scale,
+                cv::Scalar(0, 255, 255),
+                text_thickness);
+   
+  }
+  return vis_img;
+}
+
+void FlowStatistic(const MOTResult& results, const int frame_id,
+                   std::vector<int>* count_list, 
+                   std::vector<int>* in_count_list, 
+                   std::vector<int>* out_count_list) {
+  throw "Not Implement";
+}
+
+void SaveResult(const MOTResult& results, const std::string& output_dir) {
+  throw "Not Implement";
+}
+
+
+} // namespace PaddleDetection

deploy/pptracking/src/predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <iomanip>
+#include <chrono>
+#include "include/predictor.h"
+
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+void Predictor::Predict(const std::vector<cv::Mat> imgs,
+      const double threshold,
+      MOTResult* result,
+      std::vector<double>* times) {
+  if (use_jde_) {
+    jde_sct_->Predict(imgs, threshold, result, times);
+  } else {
+    sde_sct_->Predict(imgs, threshold, result, times);
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/src/preprocess_op.cc

+//   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <vector>
+#include <string>
+#include <thread>
+
+#include "include/preprocess_op.h"
+
+namespace PaddleDetection {
+
+void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
+  data->im_shape_ = {
+      static_cast<float>(im->rows),
+      static_cast<float>(im->cols)
+  };
+  data->scale_factor_ = {1., 1.};
+  data->in_net_shape_ = {
+      static_cast<float>(im->rows),
+      static_cast<float>(im->cols)
+  };
+}
+
+void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
+  double e = 1.0;
+  if (is_scale_) {
+    e /= 255.0;
+  }
+  (*im).convertTo(*im, CV_32FC3, e);
+  for (int h = 0; h < im->rows; h++) {
+    for (int w = 0; w < im->cols; w++) {
+      im->at<cv::Vec3f>(h, w)[0] =
+          (im->at<cv::Vec3f>(h, w)[0] - mean_[0] ) / scale_[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] - mean_[1] ) / scale_[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] - mean_[2] ) / scale_[2];
+    }
+  }
+}
+
+void Permute::Run(cv::Mat* im, ImageBlob* data) {
+  (*im).convertTo(*im, CV_32FC3);
+  int rh = im->rows;
+  int rw = im->cols;
+  int rc = im->channels();
+  (data->im_data_).resize(rc * rh * rw);
+  float* base = (data->im_data_).data();
+  for (int i = 0; i < rc; ++i) {
+    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
+  }
+}
+
+void Resize::Run(cv::Mat* im, ImageBlob* data) {
+  auto resize_scale = GenerateScale(*im);
+  data->im_shape_ = {
+      static_cast<float>(im->cols * resize_scale.first),
+      static_cast<float>(im->rows * resize_scale.second)
+  };
+  data->in_net_shape_ = {
+      static_cast<float>(im->cols * resize_scale.first),
+      static_cast<float>(im->rows * resize_scale.second)
+  };
+  cv::resize(
+      *im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_);
+  data->im_shape_ = {
+    static_cast<float>(im->rows),
+    static_cast<float>(im->cols),
+  };
+  data->scale_factor_ = {
+    resize_scale.second,
+    resize_scale.first,
+  };
+}
+
+
+std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
+  std::pair<float, float> resize_scale;
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+
+  if (keep_ratio_) {
+    int im_size_max = std::max(origin_w, origin_h);
+    int im_size_min = std::min(origin_w, origin_h);
+    int target_size_max = *std::max_element(target_size_.begin(), target_size_.end());
+    int target_size_min = *std::min_element(target_size_.begin(), target_size_.end());
+    float scale_min =
+        static_cast<float>(target_size_min) / static_cast<float>(im_size_min);
+    float scale_max =
+        static_cast<float>(target_size_max) / static_cast<float>(im_size_max);
+    float scale_ratio = std::min(scale_min, scale_max);
+    resize_scale = {scale_ratio, scale_ratio};
+  } else {
+    resize_scale.first =
+        static_cast<float>(target_size_[1]) / static_cast<float>(origin_w);
+    resize_scale.second =
+        static_cast<float>(target_size_[0]) / static_cast<float>(origin_h);
+  }
+  return resize_scale;
+}
+
+void LetterBoxResize::Run(cv::Mat* im, ImageBlob* data) {
+  float resize_scale = GenerateScale(*im);
+  int new_shape_w = std::round(im->cols * resize_scale);
+  int new_shape_h = std::round(im->rows * resize_scale);
+  data->im_shape_ = {
+      static_cast<float>(new_shape_h),
+      static_cast<float>(new_shape_w)
+  };
+  float padw = (target_size_[1] - new_shape_w) / 2.;
+  float padh = (target_size_[0] - new_shape_h) / 2.;
+  
+  int top = std::round(padh - 0.1);
+  int bottom = std::round(padh + 0.1);
+  int left = std::round(padw - 0.1);
+  int right = std::round(padw + 0.1);
+
+  cv::resize(
+        *im, *im, cv::Size(new_shape_w, new_shape_h), 0, 0, cv::INTER_AREA); 
+
+  data->in_net_shape_ = {
+    static_cast<float>(im->rows),
+    static_cast<float>(im->cols),
+  };
+  cv::copyMakeBorder(
+    *im,
+    *im,
+    top,
+    bottom,
+    left,
+    right,
+    cv::BORDER_CONSTANT,
+    cv::Scalar(127.5));
+
+  data->in_net_shape_ = {
+    static_cast<float>(im->rows),
+    static_cast<float>(im->cols),
+  };
+
+  data->scale_factor_ = {
+    resize_scale,
+    resize_scale,
+  };
+ 
+
+}
+
+float LetterBoxResize::GenerateScale(const cv::Mat& im) {
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+
+  int target_h = target_size_[0];
+  int target_w = target_size_[1];
+
+  float ratio_h = static_cast<float>(target_h) / static_cast<float>(origin_h);
+  float ratio_w = static_cast<float>(target_w) / static_cast<float>(origin_w);
+  float resize_scale = std::min(ratio_h, ratio_w); 
+  return resize_scale;
+}
+
+void PadStride::Run(cv::Mat* im, ImageBlob* data) {
+  if (stride_ <= 0) {
+    return;
+  }
+  int rc = im->channels();
+  int rh = im->rows;
+  int rw = im->cols;
+  int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
+  int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
+  cv::copyMakeBorder(
+    *im,
+    *im,
+    0,
+    nh - rh,
+    0,
+    nw - rw,
+    cv::BORDER_CONSTANT,
+    cv::Scalar(0));
+  data->in_net_shape_ = {
+    static_cast<float>(im->rows),
+    static_cast<float>(im->cols),
+  };
+}
+
+// Preprocessor op running order
+const std::vector<std::string> Preprocessor::RUN_ORDER = {
+  "InitInfo", "Resize", "LetterBoxResize", "NormalizeImage", "PadStride", "Permute"
+};
+
+void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
+  for (const auto& name : RUN_ORDER) {
+    if (ops_.find(name) != ops_.end()) {
+      ops_[name]->Run(im, data);
+    }
+  }
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/src/sde_predictor.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#include <sstream>
+// for setprecision
+#include <iomanip>
+#include <chrono>
+#include "include/sde_predictor.h"
+
+
+using namespace paddle_infer;
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void SDEPredictor::LoadModel(const std::string& det_model_dir,
+                             const std::string& reid_model_dir,
+                             const std::string& run_mode) {
+  throw "Not Implement";
+}
+
+
+void SDEPredictor::Preprocess(const cv::Mat& ori_im) {
+  throw "Not Implement";
+}
+
+void SDEPredictor::Postprocess(
+    const cv::Mat dets, const cv::Mat emb,
+    MOTResult* result) {
+  throw "Not Implement";
+}
+
+void SDEPredictor::Predict(const std::vector<cv::Mat> imgs,
+      const double threshold,
+      MOTResult* result,
+      std::vector<double>* times) {
+  throw "Not Implement";
+}
+
+}  // namespace PaddleDetection

deploy/pptracking/src/tracker.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <map>
+#include <stdio.h>
+#include <limits.h>
+#include <algorithm>
+
+#include "include/lapjv.h"
+#include "include/tracker.h"
+
+#define mat2vec4f(m) cv::Vec4f(*m.ptr<float>(0,0), *m.ptr<float>(0,1), *m.ptr<float>(0,2), *m.ptr<float>(0,3))
+
+namespace PaddleDetection {
+
+static std::map<int, float> chi2inv95 = {
+    {1,  3.841459f},
+    {2,  5.991465f},
+    {3,  7.814728f},
+    {4,  9.487729f},
+    {5, 11.070498f},
+    {6, 12.591587f},
+    {7, 14.067140f},
+    {8, 15.507313f},
+    {9, 16.918978f}
+};
+
+JDETracker *JDETracker::me = new JDETracker;
+
+JDETracker *JDETracker::instance(void)
+{
+    return me;
+}
+
+JDETracker::JDETracker(void) : timestamp(0), max_lost_time(30), lambda(0.98f), det_thresh(0.3f)
+{    
+}
+
+bool JDETracker::update(const cv::Mat &dets, const cv::Mat &emb, std::vector<Track> &tracks)
+{
+    ++timestamp;
+    TrajectoryPool candidates(dets.rows);
+    for (int i = 0; i < dets.rows; ++i)
+    {
+        float score = *dets.ptr<float>(i, 4);
+        const cv::Mat &ltrb_ = dets(cv::Rect(0, i, 4, 1));
+        cv::Vec4f ltrb = mat2vec4f(ltrb_);
+        const cv::Mat &embedding = emb(cv::Rect(0, i, emb.cols, 1));
+        candidates[i] = Trajectory(ltrb, score, embedding);
+    }
+
+    
+    TrajectoryPtrPool tracked_trajectories;
+    TrajectoryPtrPool unconfirmed_trajectories;
+    for (size_t i = 0; i < this->tracked_trajectories.size(); ++i)
+    {
+        if (this->tracked_trajectories[i].is_activated)
+            tracked_trajectories.push_back(&this->tracked_trajectories[i]);
+        else
+            unconfirmed_trajectories.push_back(&this->tracked_trajectories[i]);
+    }
+    
+    
+    TrajectoryPtrPool trajectory_pool = tracked_trajectories + this->lost_trajectories;
+    
+    for (size_t i = 0; i < trajectory_pool.size(); ++i)
+        trajectory_pool[i]->predict();   
+    
+    Match matches;
+    std::vector<int> mismatch_row;
+    std::vector<int> mismatch_col;
+
+    cv::Mat cost = motion_distance(trajectory_pool, candidates);
+    linear_assignment(cost, 0.7f, matches, mismatch_row, mismatch_col);
+    
+    MatchIterator miter;
+    TrajectoryPtrPool activated_trajectories;
+    TrajectoryPtrPool retrieved_trajectories;    
+
+    
+    for (miter = matches.begin(); miter != matches.end(); miter++)
+    {
+        Trajectory *pt = trajectory_pool[miter->first];
+        Trajectory &ct = candidates[miter->second];
+        if (pt->state == Tracked)
+        {
+            pt->update(ct, timestamp);
+            activated_trajectories.push_back(pt);
+        }
+        else
+        {
+            pt->reactivate(ct, timestamp);
+            retrieved_trajectories.push_back(pt);
+        }
+    }
+    
+    TrajectoryPtrPool next_candidates(mismatch_col.size());
+    for (size_t i = 0; i < mismatch_col.size(); ++i)
+        next_candidates[i] = &candidates[mismatch_col[i]];
+    
+    TrajectoryPtrPool next_trajectory_pool;
+    for (size_t i = 0; i < mismatch_row.size(); ++i)
+    {
+        int j = mismatch_row[i];
+        if (trajectory_pool[j]->state == Tracked)
+            next_trajectory_pool.push_back(trajectory_pool[j]);
+    }
+    
+    cost = iou_distance(next_trajectory_pool, next_candidates);
+    linear_assignment(cost, 0.5f, matches, mismatch_row, mismatch_col);
+    
+    for (miter = matches.begin(); miter != matches.end(); miter++)
+    {
+        Trajectory *pt = next_trajectory_pool[miter->first];
+        Trajectory *ct = next_candidates[miter->second];
+        if (pt->state == Tracked)
+        {
+            pt->update(*ct, timestamp);
+            activated_trajectories.push_back(pt);
+        }
+        else
+        {
+            pt->reactivate(*ct, timestamp);
+            retrieved_trajectories.push_back(pt);
+        }
+    }
+    
+    TrajectoryPtrPool lost_trajectories;
+    for (size_t i = 0; i < mismatch_row.size(); ++i)
+    {
+        Trajectory *pt = next_trajectory_pool[mismatch_row[i]];
+        if (pt->state != Lost)
+        {
+            pt->mark_lost();
+            lost_trajectories.push_back(pt);
+        }
+    }
+    
+    TrajectoryPtrPool nnext_candidates(mismatch_col.size());
+    for (size_t i = 0; i < mismatch_col.size(); ++i)
+        nnext_candidates[i] = next_candidates[mismatch_col[i]];
+    cost = iou_distance(unconfirmed_trajectories, nnext_candidates);
+    linear_assignment(cost, 0.7f, matches, mismatch_row, mismatch_col);
+    
+    for (miter = matches.begin(); miter != matches.end(); miter++)
+    {
+        unconfirmed_trajectories[miter->first]->update(*nnext_candidates[miter->second], timestamp);
+        activated_trajectories.push_back(unconfirmed_trajectories[miter->first]);
+    }
+    
+    TrajectoryPtrPool removed_trajectories;
+
+    for (size_t i = 0; i < mismatch_row.size(); ++i)
+    {
+        unconfirmed_trajectories[mismatch_row[i]]->mark_removed();
+        removed_trajectories.push_back(unconfirmed_trajectories[mismatch_row[i]]);
+    }
+    
+    for (size_t i = 0; i < mismatch_col.size(); ++i)
+    {
+        if (nnext_candidates[mismatch_col[i]]->score < det_thresh) continue;
+        nnext_candidates[mismatch_col[i]]->activate(timestamp);
+        activated_trajectories.push_back(nnext_candidates[mismatch_col[i]]);
+    }
+    
+    for (size_t i = 0; i < this->lost_trajectories.size(); ++i)
+    {
+        Trajectory &lt = this->lost_trajectories[i];
+        if (timestamp - lt.timestamp > max_lost_time)
+        {
+            lt.mark_removed();
+            removed_trajectories.push_back(&lt);
+        }
+    }
+    
+    TrajectoryPoolIterator piter;
+    for (piter = this->tracked_trajectories.begin(); piter != this->tracked_trajectories.end(); )
+    {
+        if (piter->state != Tracked)
+            piter = this->tracked_trajectories.erase(piter);
+        else
+            ++piter;
+    }
+    
+    this->tracked_trajectories += activated_trajectories;
+    this->tracked_trajectories += retrieved_trajectories;
+
+    this->lost_trajectories -= this->tracked_trajectories;
+    this->lost_trajectories += lost_trajectories;
+    this->lost_trajectories -= this->removed_trajectories;
+    this->removed_trajectories += removed_trajectories;
+    remove_duplicate_trajectory(this->tracked_trajectories, this->lost_trajectories);
+    
+    tracks.clear();
+    for (size_t i = 0; i < this->tracked_trajectories.size(); ++i)
+    {
+        if (this->tracked_trajectories[i].is_activated)
+        {
+            Track track = {
+                .id = this->tracked_trajectories[i].id,
+                .score = this->tracked_trajectories[i].score,
+                .ltrb = this->tracked_trajectories[i].ltrb};
+            tracks.push_back(track);
+        }
+    }
+    return 0;
+}
+
+
+cv::Mat JDETracker::motion_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b)
+{
+    if (0 == a.size() || 0 == b.size())
+        return cv::Mat(a.size(), b.size(), CV_32F);
+    
+    cv::Mat edists = embedding_distance(a, b);
+    cv::Mat mdists = mahalanobis_distance(a, b);
+    cv::Mat fdists = lambda * edists + (1 - lambda) * mdists;
+    
+    const float gate_thresh = chi2inv95[4];
+    for (int i = 0; i < fdists.rows; ++i)
+    {
+        for (int j = 0; j < fdists.cols; ++j)
+        {
+            if (*mdists.ptr<float>(i, j) > gate_thresh)
+                *fdists.ptr<float>(i, j) = FLT_MAX;
+        }
+    }
+    
+    return fdists;
+}
+
+void JDETracker::linear_assignment(const cv::Mat &cost, float cost_limit, Match &matches,
+    std::vector<int> &mismatch_row, std::vector<int> &mismatch_col)
+{
+    matches.clear();
+    mismatch_row.clear();    
+    mismatch_col.clear();
+    if (cost.empty())
+    {
+        for (int i = 0; i < cost.rows; ++i)
+            mismatch_row.push_back(i);
+        for (int i = 0; i < cost.cols; ++i)
+            mismatch_col.push_back(i);
+        return;
+    }
+    
+    float opt = 0; 
+    cv::Mat x(cost.rows, 1, CV_32S);
+    cv::Mat y(cost.cols, 1, CV_32S);    
+
+    lapjv_internal(cost, true, cost_limit,
+        (int *)x.data, (int *)y.data);
+     
+    for (int i = 0; i < x.rows; ++i)
+    {
+        int j = *x.ptr<int>(i);
+        if (j >= 0)
+            matches.insert({i, j});
+        else
+            mismatch_row.push_back(i);
+    }
+    
+    for (int i = 0; i < y.rows; ++i)
+    {
+        int j = *y.ptr<int>(i);
+        if (j < 0)
+            mismatch_col.push_back(i);
+    }
+    
+    return;
+}
+
+void JDETracker::remove_duplicate_trajectory(TrajectoryPool &a, TrajectoryPool &b, float iou_thresh)
+{
+    if (0 == a.size() || 0 == b.size())
+        return;
+    
+    cv::Mat dist = iou_distance(a, b);
+    cv::Mat mask = dist < iou_thresh;
+    std::vector<cv::Point> idx;
+    cv::findNonZero(mask, idx);
+    
+    std::vector<int> da;
+    std::vector<int> db;
+    for (size_t i = 0; i < idx.size(); ++i)
+    {
+        int ta = a[idx[i].y].timestamp - a[idx[i].y].starttime;
+        int tb = b[idx[i].x].timestamp - b[idx[i].x].starttime;
+        if (ta > tb)
+            db.push_back(idx[i].x);
+        else
+            da.push_back(idx[i].y);
+    }
+    
+    int id = 0;
+    TrajectoryPoolIterator piter;
+    for (piter = a.begin(); piter != a.end(); )
+    {
+        std::vector<int>::iterator iter = find(da.begin(), da.end(), id++);
+        if (iter != da.end())
+            piter = a.erase(piter);
+        else
+            ++piter;
+    }
+    
+    id = 0;
+    for (piter = b.begin(); piter != b.end(); )
+    {
+        std::vector<int>::iterator iter = find(db.begin(), db.end(), id++);
+        if (iter != db.end())
+            piter = b.erase(piter);
+        else
+            ++piter;
+    }
+}
+
+}   // namespace PaddleDetection

deploy/pptracking/src/trajectory.cc

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <algorithm>
+
+#include "include/trajectory.h"
+
+namespace PaddleDetection {
+
+void TKalmanFilter::init(const cv::Mat &measurement)
+{
+    measurement.copyTo(statePost(cv::Rect(0, 0, 1, 4)));
+    statePost(cv::Rect(0, 4, 1, 4)).setTo(0);
+    statePost.copyTo(statePre);
+
+    float varpos = 2 *  std_weight_position * (*measurement.ptr<float>(3));
+    varpos *= varpos;
+    float varvel = 10 * std_weight_velocity * (*measurement.ptr<float>(3));
+    varvel *= varvel;
+    
+    errorCovPost.setTo(0);
+    *errorCovPost.ptr<float>(0, 0) = varpos;
+    *errorCovPost.ptr<float>(1, 1) = varpos;
+    *errorCovPost.ptr<float>(2, 2) = 1e-4f;
+    *errorCovPost.ptr<float>(3, 3) = varpos;
+    *errorCovPost.ptr<float>(4, 4) = varvel;
+    *errorCovPost.ptr<float>(5, 5) = varvel;
+    *errorCovPost.ptr<float>(6, 6) = 1e-10f;
+    *errorCovPost.ptr<float>(7, 7) = varvel;  
+    errorCovPost.copyTo(errorCovPre);
+}
+
+const cv::Mat &TKalmanFilter::predict()
+{
+    float varpos = std_weight_position * (*statePre.ptr<float>(3));
+    varpos *= varpos;
+    float varvel = std_weight_velocity * (*statePre.ptr<float>(3));
+    varvel *= varvel;
+    
+    processNoiseCov.setTo(0);
+    *processNoiseCov.ptr<float>(0, 0) = varpos;
+    *processNoiseCov.ptr<float>(1, 1) = varpos;
+    *processNoiseCov.ptr<float>(2, 2) = 1e-4f;
+    *processNoiseCov.ptr<float>(3, 3) = varpos;
+    *processNoiseCov.ptr<float>(4, 4) = varvel;
+    *processNoiseCov.ptr<float>(5, 5) = varvel;
+    *processNoiseCov.ptr<float>(6, 6) = 1e-10f;
+    *processNoiseCov.ptr<float>(7, 7) = varvel;
+    
+    return cv::KalmanFilter::predict();
+}
+
+const cv::Mat &TKalmanFilter::correct(const cv::Mat &measurement)
+{
+    float varpos = std_weight_position * (*measurement.ptr<float>(3));
+    varpos *= varpos;
+
+    measurementNoiseCov.setTo(0);
+    *measurementNoiseCov.ptr<float>(0, 0) = varpos;
+    *measurementNoiseCov.ptr<float>(1, 1) = varpos;
+    *measurementNoiseCov.ptr<float>(2, 2) = 1e-2f;
+    *measurementNoiseCov.ptr<float>(3, 3) = varpos;
+    
+    return cv::KalmanFilter::correct(measurement);
+}
+
+void TKalmanFilter::project(cv::Mat &mean, cv::Mat &covariance) const
+{    
+    float varpos = std_weight_position * (*statePost.ptr<float>(3));
+    varpos *= varpos;
+    
+    cv::Mat measurementNoiseCov_ = cv::Mat::eye(4, 4, CV_32F);
+    *measurementNoiseCov_.ptr<float>(0, 0) = varpos;
+    *measurementNoiseCov_.ptr<float>(1, 1) = varpos;
+    *measurementNoiseCov_.ptr<float>(2, 2) = 1e-2f;
+    *measurementNoiseCov_.ptr<float>(3, 3) = varpos;
+        
+    mean = measurementMatrix * statePost;
+    cv::Mat temp = measurementMatrix * errorCovPost;
+    gemm(temp, measurementMatrix, 1, measurementNoiseCov_, 1, covariance, cv::GEMM_2_T);
+}
+
+int Trajectory::count = 0;
+
+const cv::Mat &Trajectory::predict(void)
+{
+    if (state != Tracked)
+        *cv::KalmanFilter::statePost.ptr<float>(7) = 0;
+    return TKalmanFilter::predict();
+}
+
+void Trajectory::update(Trajectory &traj, int timestamp_, bool update_embedding_)
+{
+    timestamp = timestamp_;
+    ++length;
+    ltrb = traj.ltrb;
+    xyah = traj.xyah;    
+    TKalmanFilter::correct(cv::Mat(traj.xyah));    
+    state = Tracked;
+    is_activated = true;
+    score = traj.score;   
+    if (update_embedding_)
+        update_embedding(traj.current_embedding);
+}
+
+void Trajectory::activate(int timestamp_)
+{
+    id = next_id();
+    TKalmanFilter::init(cv::Mat(xyah));    
+    length = 0;
+    state = Tracked;
+    if (timestamp_ == 1) {
+      is_activated = true;
+    }
+    timestamp = timestamp_;
+    starttime = timestamp_;
+}
+
+void Trajectory::reactivate(Trajectory &traj, int timestamp_, bool newid)
+{
+    TKalmanFilter::correct(cv::Mat(traj.xyah));
+    update_embedding(traj.current_embedding);
+    length = 0;
+    state = Tracked;
+    is_activated = true;
+    timestamp = timestamp_;
+    if (newid)
+        id = next_id();
+}
+
+void Trajectory::update_embedding(const cv::Mat &embedding)
+{
+    current_embedding = embedding / cv::norm(embedding);
+    if (smooth_embedding.empty())
+    {
+        smooth_embedding = current_embedding;
+    }
+    else
+    {
+        smooth_embedding = eta * smooth_embedding + (1 - eta) * current_embedding;
+    }
+    smooth_embedding = smooth_embedding / cv::norm(smooth_embedding);
+}
+
+TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPool &b)
+{
+    TrajectoryPool sum;
+    sum.insert(sum.end(), a.begin(), a.end());
+    
+    std::vector<int> ids(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+        ids[i] = a[i].id;
+    
+    for (size_t i = 0; i < b.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i].id);
+        if (iter == ids.end())
+        {
+            sum.push_back(b[i]);
+            ids.push_back(b[i].id);
+        }
+    }
+    
+    return sum;
+}
+
+TrajectoryPool operator+(const TrajectoryPool &a, const TrajectoryPtrPool &b)
+{
+    TrajectoryPool sum;
+    sum.insert(sum.end(), a.begin(), a.end());
+    
+    std::vector<int> ids(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+        ids[i] = a[i].id;
+    
+    for (size_t i = 0; i < b.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+        if (iter == ids.end())
+        {
+            sum.push_back(*b[i]);
+            ids.push_back(b[i]->id);
+        }
+    }
+    
+    return sum;
+}
+
+TrajectoryPool &operator+=(TrajectoryPool &a, const TrajectoryPtrPool &b)
+{    
+    std::vector<int> ids(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+        ids[i] = a[i].id;
+    
+    for (size_t i = 0; i < b.size(); ++i)
+    {
+        if (b[i]->smooth_embedding.empty())
+            continue;
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+        if (iter == ids.end())
+        {
+            a.push_back(*b[i]);
+            ids.push_back(b[i]->id);
+        }
+    }
+    
+    return a;
+}
+
+TrajectoryPool operator-(const TrajectoryPool &a, const TrajectoryPool &b)
+{
+    TrajectoryPool dif;
+    std::vector<int> ids(b.size());
+    for (size_t i = 0; i < b.size(); ++i)
+        ids[i] = b[i].id;
+    
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), a[i].id);
+        if (iter == ids.end())
+            dif.push_back(a[i]);
+    }
+    
+    return dif;
+}
+
+TrajectoryPool &operator-=(TrajectoryPool &a, const TrajectoryPool &b)
+{
+    std::vector<int> ids(b.size());
+    for (size_t i = 0; i < b.size(); ++i)
+        ids[i] = b[i].id;
+    
+    TrajectoryPoolIterator piter;
+    for (piter = a.begin(); piter != a.end(); )
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), piter->id);
+        if (iter == ids.end())
+            ++piter;
+        else
+            piter = a.erase(piter);
+    }
+    
+    return a;
+}
+
+TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b)
+{
+    TrajectoryPtrPool sum;
+    sum.insert(sum.end(), a.begin(), a.end());
+    
+    std::vector<int> ids(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+        ids[i] = a[i]->id;
+    
+    for (size_t i = 0; i < b.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i]->id);
+        if (iter == ids.end())
+        {
+            sum.push_back(b[i]);
+            ids.push_back(b[i]->id);
+        }
+    }
+    
+    return sum;
+}
+
+TrajectoryPtrPool operator+(const TrajectoryPtrPool &a, TrajectoryPool &b)
+{
+    TrajectoryPtrPool sum;
+    sum.insert(sum.end(), a.begin(), a.end());
+    
+    std::vector<int> ids(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+        ids[i] = a[i]->id;
+    
+    for (size_t i = 0; i < b.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), b[i].id);
+        if (iter == ids.end())
+        {
+            sum.push_back(&b[i]);
+            ids.push_back(b[i].id);
+        }
+    }
+    
+    return sum;
+}
+
+TrajectoryPtrPool operator-(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b)
+{
+    TrajectoryPtrPool dif;
+    std::vector<int> ids(b.size());
+    for (size_t i = 0; i < b.size(); ++i)
+        ids[i] = b[i]->id;
+    
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        std::vector<int>::iterator iter = find(ids.begin(), ids.end(), a[i]->id);
+        if (iter == ids.end())
+            dif.push_back(a[i]);
+    }
+    
+    return dif;
+}
+
+cv::Mat embedding_distance(const TrajectoryPool &a, const TrajectoryPool &b)
+{
+
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            cv::Mat u = a[i].smooth_embedding;
+            cv::Mat v = b[j].smooth_embedding;
+            double uv = u.dot(v);
+            double uu = u.dot(u);
+            double vv = v.dot(v);
+            double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+            //double dist = cv::norm(a[i].smooth_embedding, b[j].smooth_embedding, cv::NORM_L2);
+            distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+        }
+    }
+    return dists;
+}
+
+cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b)
+{
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            //double dist = cv::norm(a[i]->smooth_embedding, b[j]->smooth_embedding, cv::NORM_L2);
+            //distsi[j] = static_cast<float>(dist);
+            cv::Mat u = a[i]->smooth_embedding;
+            cv::Mat v = b[j]->smooth_embedding;
+            double uv = u.dot(v);
+            double uu = u.dot(u);
+            double vv = v.dot(v);
+            double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+            distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat embedding_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b)
+{
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            //double dist = cv::norm(a[i]->smooth_embedding, b[j].smooth_embedding, cv::NORM_L2);
+            //distsi[j] = static_cast<float>(dist);
+            cv::Mat u = a[i]->smooth_embedding;
+            cv::Mat v = b[j].smooth_embedding;
+            double uv = u.dot(v);
+            double uu = u.dot(u);
+            double vv = v.dot(v);
+            double dist = std::abs(1. - uv / std::sqrt(uu * vv));
+            distsi[j] = static_cast<float>(std::max(std::min(dist, 2.), 0.));
+
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPool &a, const TrajectoryPool &b)
+{
+    std::vector<cv::Mat> means(a.size());
+    std::vector<cv::Mat> icovariances(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        cv::Mat covariance;
+        a[i].project(means[i], covariance);
+        cv::invert(covariance, icovariances[i]);
+    }
+
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Mat x(b[j].xyah);
+            float dist = static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+            distsi[j] = dist * dist;
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b)
+{
+    std::vector<cv::Mat> means(a.size());
+    std::vector<cv::Mat> icovariances(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        cv::Mat covariance;
+        a[i]->project(means[i], covariance);
+        cv::invert(covariance, icovariances[i]);
+    }
+
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Mat x(b[j]->xyah);
+            float dist = static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+            distsi[j] = dist * dist;
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat mahalanobis_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b)
+{
+    std::vector<cv::Mat> means(a.size());
+    std::vector<cv::Mat> icovariances(a.size());
+ 
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        cv::Mat covariance;
+        a[i]->project(means[i], covariance);
+        cv::invert(covariance, icovariances[i]);
+    }
+    
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Mat x(b[j].xyah);
+            float dist = static_cast<float>(cv::Mahalanobis(x, means[i], icovariances[i]));
+            distsi[j] = dist * dist;
+        }
+    }
+    
+    return dists;
+}
+
+static inline float calc_inter_area(const cv::Vec4f &a, const cv::Vec4f &b)
+{
+    if (a[2] < b[0] || a[0] > b[2] || a[3] < b[1] || a[1] > b[3])
+        return 0.f;
+    
+    float w = std::min(a[2], b[2]) - std::max(a[0], b[0]);
+    float h = std::min(a[3], b[3]) - std::max(a[1], b[1]);
+    return w * h;
+}
+
+cv::Mat iou_distance(const TrajectoryPool &a, const TrajectoryPool &b)
+{
+    std::vector<float> areaa(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float w = a[i].ltrb[2] - a[i].ltrb[0];
+        float h = a[i].ltrb[3] - a[i].ltrb[1];
+        areaa[i] = w * h;
+    }
+    
+    std::vector<float> areab(b.size());
+    for (size_t j = 0; j < b.size(); ++j)
+    {
+        float w = b[j].ltrb[2] - b[j].ltrb[0];
+        float h = b[j].ltrb[3] - b[j].ltrb[1];
+        areab[j] = w * h;
+    }
+    
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        const cv::Vec4f &boxa = a[i].ltrb;
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Vec4f &boxb = b[j].ltrb;
+            float inters = calc_inter_area(boxa, boxb);
+            distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPtrPool &b)
+{
+    std::vector<float> areaa(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float w = a[i]->ltrb[2] - a[i]->ltrb[0];
+        float h = a[i]->ltrb[3] - a[i]->ltrb[1];
+        areaa[i] = w * h;
+    }
+    
+    std::vector<float> areab(b.size());
+    for (size_t j = 0; j < b.size(); ++j)
+    {
+        float w = b[j]->ltrb[2] - b[j]->ltrb[0];
+        float h = b[j]->ltrb[3] - b[j]->ltrb[1];
+        areab[j] = w * h;
+    }
+    
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        const cv::Vec4f &boxa = a[i]->ltrb;
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Vec4f &boxb = b[j]->ltrb;
+            float inters = calc_inter_area(boxa, boxb);
+            distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+        }
+    }
+    
+    return dists;
+}
+
+cv::Mat iou_distance(const TrajectoryPtrPool &a, const TrajectoryPool &b)
+{
+    std::vector<float> areaa(a.size());
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        float w = a[i]->ltrb[2] - a[i]->ltrb[0];
+        float h = a[i]->ltrb[3] - a[i]->ltrb[1];
+        areaa[i] = w * h;
+    }
+    
+    std::vector<float> areab(b.size());
+    for (size_t j = 0; j < b.size(); ++j)
+    {
+        float w = b[j].ltrb[2] - b[j].ltrb[0];
+        float h = b[j].ltrb[3] - b[j].ltrb[1];
+        areab[j] = w * h;
+    }
+    
+    cv::Mat dists(a.size(), b.size(), CV_32F);
+    for (size_t i = 0; i < a.size(); ++i)
+    {
+        const cv::Vec4f &boxa = a[i]->ltrb;
+        float *distsi = dists.ptr<float>(i);
+        for (size_t j = 0; j < b.size(); ++j)
+        {
+            const cv::Vec4f &boxb = b[j].ltrb;
+            float inters = calc_inter_area(boxa, boxb);
+            distsi[j] = 1.f - inters / (areaa[i] + areab[j] - inters);
+        }
+    }
+    
+    return dists;
+}
+
+}   // namespace PaddleDetection